Physicists in biology, inverse problems and other quirks of the genomic age

Nobel Laureate Sydney Brenner has 
criticized systems biology as a grandiose 
attempt to solve inverse problems in biology

Leo Szilard – brilliant, peripatetic Hungarian physicist, habitué of hotel lobbies, soothsayer without peer – first grasped the implications of a nuclear chain reaction in 1933 while stepping off the curb at a traffic light in London. Szilard has many distinctions to his name; not only did he file a patent for the first nuclear reactor with Enrico Fermi, but he was the one who urged his old friend Albert Einstein to write a famous letter to Franklin Roosevelt, and also the one who tried to get another kind of letter signed as the war was ending in 1945; a letter urging the United States to demonstrate a nuclear weapon in front of the Japanese before irrevocably stepping across the line. Szilard was successful in getting the first letter signed but failed in his second goal.

After the war ended, partly disgusted by the cruel use to which his beloved physics had been put, Szilard left professional physics to explore new pastures – in his case, biology. But apart from the moral abhorrence which led him to switch fields, there was a more pragmatic reason. As Szilard put it, this was an age when you took a year to discover something new in physics but only took a day to discover something new in biology.

This sentiment drove many physicists into biology, and the exodus benefited biological science spectacularly. Compared to physics whose basic theoretical foundations had matured by the end of the war, biology was uncharted territory. The situation in biology was similar to the situation during the heyday of physics right after the invention of quantum theory when, as Paul Dirac quipped, “even second-rate physicists could make first-rate discoveries”. And physicists took full advantage of this situation. Since Szilard, biology in general and molecular biology in particularly have been greatly enriched by the presence of physicists. Today, any physics student who wants to mull doing biology stands on the shoulders of illustrious forebears including Szilard, Erwin Schrodinger, Francis Crick, Walter Gilbert and most recently, Venki Ramakrishnan.

What is it that draws physicists to biology and why have they been unusually successful in making contributions to it? The allure of understanding life which attracts other kinds of scientists is certainly one motivating factor. Erwin Schrodinger whose little book “What is Life?” propelled many including Jim Watson and Francis Crick into genetics is one example. Then there is the opportunity to simplify an enormously complex system into its constituent parts, an art which physicists have excelled at since the time of the Greeks. Biology and especially the brain is the ultimate complex system, and physicists are tempted to apply their reductionist approaches to deconvolute this complexity. Thirdly there is the practical advantage that physicists have; a capacity to apply experimental tools like x-ray diffraction and quantitative reasoning including mathematical and statistical tools to make sense of biological data.

The rise of the data scientists

It it this third reason that has led to a significant influx of not just physicists but other quantitative scientists, including statisticians and computer scientists, into biology. The rapid development of the fields of bioinformatics and computational biology has led to a great demand for scientists with the quantitative skills to analyze large amounts of data. A mathematical background brings valuable skills to this endeavor and quantitative, data-driven scientists thrive in genomics. Eric Lander for instance got his PhD in mathematics at Oxford before – driven by the tantalizing goal of understanding the brain – he switched to biology. Cancer geneticist Bert Vogelstein also has a background in mathematics. All of us are familiar with names like Craig Venter, Francis Collins and James Watson when it comes to appreciating the cracking of the human genome, but we need to pay equal attention to the computer scientists without whom crunching and combining the immense amounts of data arising from sequencing would have been impossible. There is no doubt that, after the essentially chemically driven revolution in genetics of the 70s, the second revolution in the field has been engineered by data crunching.

So what does the future hold? The rise of the “data scientists” has led to the burgeoning field of systems biology, a buzzword which seems to proliferate more than its actual understanding. Systems biology seeks to integrate different kinds of biological data into a broad picture using tools like graph theory and network analysis. It promises to potentially provide us with a big-picture view of biology like no other. Perhaps, physicists think, we will have a theoretical framework for biology that does what quantum theory did for, say, chemistry.

Emergence and systems biology: A delicate pairing

And yet even as we savor the fruits of these higher-level approaches to biology, we must be keenly aware of their pitfalls. One of the fundamental truths about the physicists’ view of biology is that it is steeped in reductionism. Reductionism is the great legacy of modern science which saw its culmination in the two twentieth-century scientific revolutions of quantum mechanics and molecular biology. It is hard to overstate the practical ramifications of reductionism. And yet as we tackle the salient problems in twenty-first century biology, we are become aware of the limits of reductionism. The great antidote to reductionism is emergence, a property that renders complex systems irreducible to the sum of their parts. In 1972 the Nobel Prize winning physicist Philip Anderson penned a remarkably far-reaching article named “More is Different” which explored the inability of “lower-level” phenomena to predict their “higher-level” manifestations.

The brain is an outstanding example of emergent phenomena. Many scientists think that neuroscience is going to be to the twenty-first century what molecular biology was to the twentieth. For the first time in history, partly through recombinant DNA technology and partly due to state-of-the-art imaging techniques like functional MRI, we are poised on the brink of making major discoveries about the brain; no wonder that Francis Crick moved into neuroscience during his later years. But the brain presents a very different kind of challenge than that posed by, say, a superconductor or a crystal of DNA. The brain is a highly hierarchical and modular structure, with multiple dependent and yet distinct layers of organization. From the basic level of the neuron we move onto collections of neurons and glial cells which behave very differently, onward to specialized centers for speech, memory and other tasks on to the whole brain. As we move up this ladder of complexity, emergent features arise at every level whose behavior cannot be gleaned merely from the behavior of individual neurons.

The tyranny of inverse problems

The problem thwarts systems biology in general. In recent years, some of the most insightful criticism of systems biology has come from Sydney Brenner, a founding father of molecular biology whose 2010 piece in Philosophical Transactions of the Royal Society titled “Sequences and Consequences” should be required reading for those who think that systems biology’s triumph is just around the corner. In his essay, Brenner strikes at what he sees as the heart of the goal of systems biology. After reminding us that the systems approach seeks to generate viable models of living systems, Brenner goes on to say that:

“Even though the proponents seem to be unconscious of it, the claim of systems biology is that it can solve the inverse problem of physiology by deriving models of how systems work from observations of their behavior. It is known that inverse problems can only be solved under very specific conditions. A good example of an inverse problem is the derivation of the structure of a molecule from the X-ray diffraction pattern of a crystal…The universe of potential models for any complex system like the function of a cell has very large dimensions and, in the absence of any theory of the system, there is no guide to constrain the choice of model.”

What Brenner is saying that every systems biology project essentially results in a model, a model that tries to solve the problem of divining reality from experimental data. However, a model is not reality; it is an imperfect picture of reality constructed from bits and pieces of data. It is therefore – and this has to be emphasized – only one representation of reality. Other models might satisfy the same experimental constraints and for systems with thousands of moving parts like cells and brains, the number of models is astronomically large. In addition, data in biological measurements is often noisy with large error bars, further complicating its use. This puts systems biology into the classic conundrum of the inverse problem that Brenner points out, and like other inverse problems, the solution you find is likely to be one among an expanding universe of solutions, many of which might be better than the one you have. This means that while models derived from systems biology might be useful – and often this is a sufficient requirement for using them – they might likely leave out some important feature of the system.

There has been some very interesting recent work in addressing such conundrums. One of the major challenges in the inverse problem universe is to find a minimal set of parameters that can describe a system. Ideally the parameters should be sensitive to variation so that one constrains the parameter space describing the given system and avoids the "anything goes" trap. A particularly promising example is the use of 'sloppy models' developed by Cornell physicist James Sethna and others in which parameter combinations rather than individual parameters are varied and those combinations which are most tightly constrained are then picked as the 'right' ones.

But quite apart from these theoretical fixes, Brenner’s remedy for avoiding the fallout from imperfect systems modeling is to simply use the techniques garnered from classical biochemistry and genetics over the last century or so. In one sense systems biology is nothing new; as Brenner tartly puts it, “there is a watered-down version of systems biology which does nothing more than give a new name to physiology, the study of function and the practice of which, in a modern experimental form, has been going on at least since the beginning of the Royal Society in the seventeenth century”. Careful examination of mutant strains of organisms, measurement of the interactions of proteins with small molecules like hormones, neurotransmitters and drugs, and observation of phenotypic changes caused by known genotypic perturbations remain tried-and-tested ways of drawing conclusions about the behavior of living systems on a molecular scale.

Genomics and drug discovery: Tread softly

This viewpoint is also echoed by those who take a critical view of what they say is an overly genomics-based approach to the treatment of diseases. A particularly clear-headed view comes from Gerry Higgs who in 2004 presciently wrote a piece titled “Molecular Genetics: The Emperor’s Clothes of Drug Discovery”. Higgs criticizes the whole gamut of genomic tools used to discover new therapies, from the “high-volume, low-quality sequence data” to the genetically engineered cell lines which can give a misleading impression of molecular interactions under normal physiological conditions. Higgs points to many successful drugs discovered in the last fifty years which have been found using the tools of classical pharmacology and biochemistry; these would include the best-selling, Nobel Prize winning drugs developed by Gertrude Elion and James Black based on simple physiological assays. Higgs’s point is that the genomics approach to drugs runs the risk of becoming too reductionist and narrow-minded, often relying on isolated systems and artificial constructs that are uncoupled from whole systems. His prescription is not to discard these tools which can undoubtedly provide important insights, but supplement them with older and proven physiological experiments.

Does all this mean that systems biology and genomics would be useless in leading us to new drugs? Not at all. There is no doubt that genomic approaches can be remarkably useful in enabling controlled experiments. The systems biologist Leroy Hood for instance has pointed out how selective gene silencing can allow us to tease apart side-effects of drugs from beneficial ones. But what Higgs, Brenner and others are impressing upon us is that we shouldn’t allow genomics to become the end-all and be-all of drug discovery. Genomics should only be employed as part of a judiciously chosen cocktail of techniques including classical ones for interrogating the function of living systems. And this applies more generally to physics-based and systems biology approaches. 

Perhaps the real problem from which we need to wean ourselves is “physics envy”; as the physicist-turned-financial modeler Emanuel Derman reminds us, “Just like  physicists, we would like to discover three laws that govern ninety-nine percent of our system’s intricacies. But we are more likely to discover ninety-nine laws that explain three percent of our system”. And that’s as good a starting point as any.

Adapted from a previous post on Scientific American Blogs.

On physics envy and drug discovery

In a recent New York Times article, two prominent social scientists lament the epidemic of physics envy that has infected their ranks, and they implore their colleagues to take a more observation-based, utilitarian approach to addressing the most pressing problems of social science.

We natural scientists should empathize. Physics envy is the name of a disease that afflicts many scientists at various stages of their careers. Its main symptom is an overwhelming desire to see their science - whatever it may be - become as precise and predictable as particle physics. The victim of physics envy thinks wistfully of the glorious days of quantum mechanics and molecular biology and believes that his or her science can achieve the same six-decimal precision in its measurements and predictions. The victims may be natural or social scientists, although the disease takes on a particularly nasty form when it affects economists, as recounted by the physicist-turned-financial modeler Emanuel Derman.

Physics envy is so widespread that even physicists are affected by it. Some theoretical physicists for instance want to reduce all the world's complexities to an all-encompassing theory of "everything", preferably a single equation that would describe everything from black holes to romantic love. Presumably this will help us truly understand, from first principles, why nations go to war or why election outcomes depend on Ohio. This is in part because physics envy is closely tied to its cousin reductionism which provided untold dividends in twentieth century science. But the scientific world in the twenty-first century is a different creature. Physics envy during our times can cause tunnel vision, an exaggerated belief in the power of mathematics, and not infrequently, the loss of billions of dollars. Perhaps the worst thing about this malady may be its focused transmission to new generations of students and scientists, thus ensuring its long life and continued dominance.

Sadly, this disease is not unknown among drug discovery scientists, and I dare say that I have suffered from it myself. Drug discovery is a complex, multidisciplinary field where luck and intuition play as great a role as any rational approach. Drug hunters study complex systems that are almost always refractory to any one approach from any one science. Yet physics envy exists, implicitly or explicitly. You see it in the modeler who thinks he can find the next revolutionary drug simply by optimizing his compound's affinity for his protein, or the synthetic chemist who thinks that he can produce an army of molecular analogs that can dissect a complete biological pathway, or the biologist who thinks that inhibiting his pet protein will be all that it takes to disable a complicated biochemical pathway.

A more serious case is the scientist who thinks that if only we had knowledge of every single biological and chemical building block, if only we could map out every gene, every protein, every small molecule interacting with all these genes and proteins and present these interactions on a wall like a subway map, we will be able to understand and treat all diseases. These scientists who often but not always go by the name of systems biologists, try to produce precisely this kind of map and predict the output from an input. The output can be in the form of an upregulated protein or the manifestation of a phenotype. The input may be the activation of a gene or inhibition of a protein by a small molecule. The systems biologists think that what's necessary (and perhaps sufficient) for predicting responses in a biological system is a map.

Yet as the Nobel Prize winning biologist Sydney Brenner once wrote in a very readable article, he has been practicing "systems biology" all his life, except that in his time it was called "physiology". Brenner is supposedly practicing systems biology without a license, and yet he and many of his fellow classical physiologists seem to have been both remarkably successful and strangely immune to physics envy. Their job was to study the responses of biological systems using every tool at their disposal. It did not matter if they didn't have an overwhelming theoretical framework to tie together their diverse observations. As the authors of the NYT article indicate, the lack of deep theory does not preclude either understanding or utility. An example of this principle would be the school of pharmacology starting with Steve Brodie at the NIH and culminating with Solomon Snyder at Johns Hopkins. As illustrated in Robert Kanigel's book, these scientists made important discoveries in basic pharmacology even when detailed knowledge of genes and proteins was unavailable. They did not need the pharmacological equivalent of a "theory of everything" to proceed. In fact they did not even need a theory in some cases.

The same goes for drug discovery in general. Think of the currently fashionable paradigm of phenotypic screening which involves discovering new compounds by looking at their effects on simple phenotypic traits like locomotion or heart rate. These approaches hark back to the old days of drug discovery when the responses could be purely clinical, anything from increased urination to dry mouth to flushed faces. This approach is very far from the target-based reductionist approaches that have become popular in the last twenty years, yet nobody can deny its value.

But neither are target-based approaches useless. If you are dealing with HIV protease which can yield copious crystal structures, a structure-based approach might be, and indeed was, very fruitful. But think of a protein whose binding pocket is unknown, which is full of flexible regions and whose functional form consists of oligomers of unknown composition, and structure-based approaches might be completely useless. It might then be best to proceed based on the biology alone or by educated guesswork guided by SAR trends.

The point is, it all depends on the specific case. And therein lies the rub of drug discovery and the problem of physics envy. As I mentioned in a previous post, physics searches for general principles while drug discovery largely thrives on exceptions. The question in drug discovery is not what overarching principle can be applied to all cases, but what exact mix of different techniques would work for a given case. It's very different from physics, where the goal is to search for one equation to rule them all.

Fortunately physics envy has a potent antidote, both in physics and in drug discovery. It goes by the name of "nature". Whenever physics envy tries to go on a rampage, nature invariably steps in and straps on the straitjacket. In just the last few years we have seen nature generously cutting us down to size. From resveratrol-based SIRT inhibitors for aging to recently tainted PARP inhibitors for cancer, we have seen how nature (which in most of these cases means "biology") is smarter than us. Every time we break down a system into its constituent parts and pin down what we think is the operative entity, nature keeps reminding us that there's something else out there which is equally important which we have missed. In some sense nature is mocking us for bringing our biases to bear on what in her scheme of things is only one important component of a grand dance. In nature's eyes the rule is simple; if we want to participate, we better make sure we know who our partner is. And leave physics envy at the door.